1 1 MPI for Biological Cybernetics 2 Stanford University 3 Werner Reichardt Centre for Integrative Neuroscience Eberhard Karls University Tuebingen Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis M. Gomez-Rodriguez 1,2 M. Grosse-Wentrup 1 J. Peters 1 G. Naros 3 J. Hill 1 B. Schölkopf 1 A. Gharabaghi 3 ICPR Workshop in Brain Decoding, August 2010

2 BCI for Neurorehabilitation A Brain Computer Interface (BCI) for neurorehabilitation of: Hemiparetic syndromes due to cerebrovascular, traumatic or tumor-related brain damage. may outperform traditional therapy → Hebbian rule-based [1] 1.Instantaneous feedback: Synchronize subject’s attempt and feedback 2.High accuracy: User’s control of the BCI 3.High specificity: To focus on specific areas of the cortex 4.Limited invasiveness Challenges

3 EECoG for Neurorehabilitation BCI with Epidural Electrocorticography (EECoG) may be a promising tool for neurorehabilitation. Instantaneous feedback: Delays in the order of ms. Why EECoG for neurorehabilitation? High accuracy: On-line decoding of arm movement intention. High specificity: Greater spatial resolution over motor cortex. Limited invasiveness: Safer alternative to intraparenchymal electrodes or subdural devices. Previous studies [2, 3]: Off-line real movement and motor imagery classification and real movement direction decoding using subdural ECoG with epileptic patients. In our work: On-line movement attempt classification using epidural ECoG with a stroke patient.

4 Outline We present a Case Study (one patient) of a BCI with Epidural Electrocorticography (EECoG): 1.Experimental Setup: Human subject, task and recording 2.Methods: Signal processing and on-line decoding techniques 3.Results: Spatial and frequency features and classification performance 4.Conclusions

5 Experimental Setup Recording: 96 epidural ECoG platinum electrodes: somato- sensory, motor and pre-motor cortex. 8 x 12-electrode grid, 4-mm diameter pad (2.3 mm exposed), 5-mm interelectrode distance. Human subject: 65-year old male, right-sided hemiparesis (hemorrhagic stroke in left thalamus). Subject’s task: attempt to move the right arm forward (extension) or backward (flexion). Training & test phase in each run, 30 trials per run, 5-s movement + 3-s rest per trial Limited invasiveness!

6 Methods (Signal Processing)  Common-Average Reference (CAR), band-pass filtering (2- 115Hz) & notch filtering (50 Hz power-line).  Average power spectral densities over 2Hz frequency bins for each electrode are used as features.  Welch’s method over overlapping incrementally bigger time segments each 5-s movement or 3-s resting periods. Larger segments → Less noise and more reliable estimates. Shorter segments → Necessary for on-line feedback.

7 Methods (On-line Decoding) On-line classification (every 300ms) between movement and resting using spectral features: Visual on-line feedback is provided A linear support vector machine (SVM) is generated on-line after a training period (15 seconds of each condition)

8 Results (Spatial and Spectral Features) Discriminative power of each electrode & frequency bin: Operating Characteristic Curve (AUC), 96 electrodes, (2-115) Hz Classifier weights, 35 electrodes, (2-80) Hz Low freqs (<40 Hz): Power decreases for movement attempt High freqs (>40 Hz): Power increases for movement attempt

9 Results (Spatial and Spectral Features) To see discriminative power of each spatial location better: Average AUC for each electrode over: Low freqs ( Hz)High freqs ( Hz) High Specificity!

10 Results (Performance) On-line decoding of movement intention based on different frequency bands: (2, 40), (40, 80) and (2, 80) Hz High Accuracy!

11 Conclusions  We showed the feasibility of epidural ECoG for BCI- based rehabilitation devices for hemiparetic patients  Future work (in progress):  EECoG on-line decoding + haptic feedback provided by a robot arm guiding patient’s arm:  See already workshop paper at SMC’10 [4] on robot-based haptic feedback using EEG on-line decoding on healthy subjects. Instantaneous feedback: delay of 300 ms. High accuracy: > 85 % in arm movement intention decoding. High specificity: Cortical reorganization caused by the stroke. Limited invasiveness

12 References [1]T. H. Murphy, and D. Corbett. Plasticity during stroke recovery: from synapse to behaviour. Nature Review Neuroscience 2009, 10-12, [2] T. Pistohl, T. Ball, A. Schulze-Bonhage, A. Aertsen, and C. Mehring. Prediction of arm movement trajectories from ECoG-recordings in humans. Journal of Neuroscience Methods 2008, Vol , [3] G. Schalk, J. Kubanek, K.J. Miller, N.R. Anderson, E.C. Leuthardt, J.G. Ojemann, D. Limbrick, D. Moran, L.A. Gerhardt, and J.R. Wolpaw. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. Journal of Neural Engineering 2007, [4] M. Gomez-Rodriguez, J. Peters, J. Hill, B. Schölkopf, A. Gharabaghi, and M. Grosse-Wentrup. Closing the Sensorimotor Loop: Haptic Feedback Facilitates Decoding of Arm Movement Imagery. SMC Workshop in Shared-Control for BMI, 2010.